------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\jar68\OneDrive\Ongoing Work\Party Cues and Suspicion Paper\Final Datavserse\Online Appendix E\appendix_oe_log.log
  log type:  text
 opened on:  23 Jun 2021, 17:07:28

. use "comb_educ.dta"

. set more off

. 
. /**********************************************
> Figure 1
> **********************************************/
. 
. /****Some Initial Cleaning*****/
. label var treat_3 "Treatment Condition"

. label var support01 "Policy Support"

. 
. label def edu 1 "HS or Less" 2 "Some College" 3 "BA" 4 "Post-BA"

. label values educ edu

. 
. 
. gen polissue = . 
(6,151 missing values generated)

.         replace polissue = 1 if experiment == "Experiment 1"
(1,336 real changes made)

.         replace polissue = 2 if experiment == "Experiment 2"  & pol_exp == "Tax:Conservative"
(1,055 real changes made)

.         replace polissue = 3 if experiment == "Experiment 2" & pol_exp == "Tax:Liberal"
(1,053 real changes made)

.         replace polissue = 4 if experiment == "Experiment 3" & pol_exp == "Tax:Conservative"
(1,216 real changes made)

.         replace polissue = 5 if experiment == "Experiment 3"& pol_exp == "Tax:Liberal"
(1,244 real changes made)

. label var polissue "Experiment & Issue"

. label def pol1 1 "Experiment 1" 2 "Exp 2: Conservative Change" ///
>                                 3 "Exp 2: Liberal Change" 4 "Exp 3: Conservative Change" ///
>                                 5 "Exp 3: Liberal Change"

. label values polissue pol1

. 
. label var treat_policy "Policy Treatment"

. 
. /******Models******/
. eststo clear

. regress support01 i.treat_3 i.polissue i.educ

      Source |       SS           df       MS      Number of obs   =     5,048
-------------+----------------------------------   F(9, 5038)      =      8.26
       Model |  6.00757008         9  .667507787   Prob > F        =    0.0000
    Residual |  407.026988     5,038  .080791383   R-squared       =    0.0145
-------------+----------------------------------   Adj R-squared   =    0.0128
       Total |  413.034558     5,047  .081837638   Root MSE        =    .28424

---------------------------------------------------------------------------------------------
                  support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                    treat_3 |
                 Party Cue  |   .0761625    .010945     6.96   0.000     .0547056    .0976195
         Cue w/Insinuation  |    .025099   .0099381     2.53   0.012     .0056161     .044582
                            |
                   polissue |
Exp 2: Conservative Change  |   .0066534   .0135013     0.49   0.622    -.0198151    .0331219
     Exp 2: Liberal Change  |  -.0061773   .0134839    -0.46   0.647    -.0326116     .020257
Exp 3: Conservative Change  |   .0332233   .0124333     2.67   0.008     .0088486     .057598
     Exp 3: Liberal Change  |   .0044495   .0123782     0.36   0.719    -.0198171    .0287162
                            |
                       educ |
              Some College  |  -.0027436    .011228    -0.24   0.807    -.0247555    .0192682
                        BA  |   .0068696   .0118742     0.58   0.563    -.0164089    .0301481
                   Post-BA  |   .0456191   .0144171     3.16   0.002     .0173553    .0738828
                            |
                      _cons |   .4597354   .0140392    32.75   0.000     .4322125    .4872583
---------------------------------------------------------------------------------------------

.         estimates store m1

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0510635   .0099611     5.13   0.000     .0315354    .0705916
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .05106351

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  5.126285

. 
. regress support01 i.treat_3 i.educ if experiment == "Experiment 1"

      Source |       SS           df       MS      Number of obs   =     1,013
-------------+----------------------------------   F(5, 1007)      =      1.36
       Model |  .497234438         5  .099446888   Prob > F        =    0.2378
    Residual |  73.7514219     1,007  .073238751   R-squared       =    0.0067
-------------+----------------------------------   Adj R-squared   =    0.0018
       Total |  74.2486564     1,012  .073368238   Root MSE        =    .27063

------------------------------------------------------------------------------------
         support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           treat_3 |
        Party Cue  |   .0438133   .0211291     2.07   0.038     .0023513    .0852754
Cue w/Insinuation  |    .000802   .0206168     0.04   0.969    -.0396548    .0412589
                   |
              educ |
     Some College  |  -.0236418   .0283409    -0.83   0.404    -.0792558    .0319722
               BA  |  -.0296495   .0281176    -1.05   0.292    -.0848253    .0255262
          Post-BA  |  -.0306047   .0327438    -0.93   0.350    -.0948587    .0336492
                   |
             _cons |   .5110066    .026775    19.09   0.000     .4584653    .5635478
------------------------------------------------------------------------------------

.         estimates store m2

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0430113   .0209135     2.06   0.040     .0019722    .0840504
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .0430113

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  2.056625

.         
. regress support01 i.treat_3 i.educ i.treat_policy if experiment == "Experiment 2"

      Source |       SS           df       MS      Number of obs   =     1,582
-------------+----------------------------------   F(6, 1575)      =      3.76
       Model |  1.73112483         6  .288520804   Prob > F        =    0.0010
    Residual |  120.745234     1,575  .076663641   R-squared       =    0.0141
-------------+----------------------------------   Adj R-squared   =    0.0104
       Total |  122.476359     1,581  .077467653   Root MSE        =    .27688

------------------------------------------------------------------------------------
         support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           treat_3 |
        Party Cue  |   .0789655   .0170766     4.62   0.000     .0454703    .1124607
Cue w/Insinuation  |    .036593   .0170522     2.15   0.032     .0031457    .0700403
                   |
              educ |
     Some College  |  -.0142237   .0233708    -0.61   0.543    -.0600648    .0316174
               BA  |  -.0078264   .0231514    -0.34   0.735    -.0532372    .0375843
          Post-BA  |  -.0211085   .0277889    -0.76   0.448    -.0756157    .0333987
                   |
      treat_policy |
   Liberal Change  |  -.0105099   .0139638    -0.75   0.452    -.0378995    .0168798
             _cons |   .4792901   .0231349    20.72   0.000     .4339117    .5246686
------------------------------------------------------------------------------------

.         estimates store m3

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0423725   .0170553     2.48   0.013     .0089191    .0758259
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .0423725

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  2.4844212

.         
. regress support01 i.treat_3 i.educ i.treat_policy if experiment == "Experiment 3"

      Source |       SS           df       MS      Number of obs   =     2,453
-------------+----------------------------------   F(6, 2446)      =     11.90
       Model |  6.12768207         6  1.02128035   Prob > F        =    0.0000
    Residual |  209.962754     2,446  .085839229   R-squared       =    0.0284
-------------+----------------------------------   Adj R-squared   =    0.0260
       Total |  216.090436     2,452  .088128237   Root MSE        =    .29298

------------------------------------------------------------------------------------
         support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           treat_3 |
        Party Cue  |   .0975956   .0186741     5.23   0.000      .060977    .1342142
Cue w/Insinuation  |   .0325738   .0152749     2.13   0.033     .0026207    .0625268
                   |
              educ |
     Some College  |  -.0000773   .0146659    -0.01   0.996    -.0288361    .0286815
               BA  |   .0158384   .0168262     0.94   0.347    -.0171567    .0488336
          Post-BA  |   .1161338   .0205243     5.66   0.000      .075887    .1563806
                   |
      treat_policy |
   Liberal Change  |  -.0290353   .0118347    -2.45   0.014    -.0522424   -.0058282
             _cons |   .4730598   .0172004    27.50   0.000     .4393309    .5067886
------------------------------------------------------------------------------------

.         estimates store m4

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0650219   .0152558     4.26   0.000     .0351062    .0949375
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .06502185

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  4.2621006

. 
. /*******Table******/
. esttab m1 m2 m3 m4 using "table_oe1_table.rtf", replace ///
>         onecell label nobaselevels b(2) se star(* 0.05 ** 0.01 *** 0.001) ///
>         mtitles("Pooled" "Exp. 1" "Exp. 2" "Exp. 3") ///
>         title("{\b Table OE1:} Figure 1 Analyses with Education Controls") ///
>         stats(N diff sig) ///
>         addnotes("The baseline category for the cue treatment is the No Cue condition. The base category for experiment/policy in the Pooled model is Experiment 1. Th
> e base category for the policy treatment in Exp. 2 and 3 models is the Conservative Change condition")
(note: file table_oe1_table.rtf not found)
(output written to table_oe1_table.rtf)

.         
. 
. /**********************************************
> Figure 2
> **********************************************/
. 
. encode experiment, gen(exp)

. 
. /*****Models******/
. eststo clear

. eststo: regress support01 i.treat_3##i.stereo i.exp i.educ

      Source |       SS           df       MS      Number of obs   =     4,034
-------------+----------------------------------   F(9, 4024)      =     21.30
       Model |  15.4048261         9  1.71164735   Prob > F        =    0.0000
    Residual |  323.289098     4,024  .080340233   R-squared       =    0.0455
-------------+----------------------------------   Adj R-squared   =    0.0433
       Total |  338.693924     4,033  .083980641   Root MSE        =    .28344

--------------------------------------------------------------------------------------------------
                       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         treat_3 |
                      Party Cue  |   .0968062   .0176366     5.49   0.000     .0622287    .1313836
              Cue w/Insinuation  |   .0344903   .0155787     2.21   0.027     .0039475    .0650331
                                 |
                          stereo |
                  Stereotypical  |   .1045753   .0177708     5.88   0.000     .0697347    .1394159
                                 |
                  treat_3#stereo |
        Party Cue#Stereotypical  |  -.0187358    .025122    -0.75   0.456    -.0679888    .0305173
Cue w/Insinuation#Stereotypical  |  -.0043594   .0218365    -0.20   0.842     -.047171    .0384521
                                 |
                             exp |
                   Experiment 3  |   .0208987   .0097207     2.15   0.032     .0018408    .0399566
                                 |
                            educ |
                   Some College  |  -.0002622   .0121138    -0.02   0.983     -.024012    .0234876
                             BA  |   .0161832   .0130402     1.24   0.215    -.0093828    .0417491
                        Post-BA  |   .0641977   .0159657     4.02   0.000     .0328962    .0954992
                                 |
                           _cons |   .3944001   .0165491    23.83   0.000     .3619547    .4268456
--------------------------------------------------------------------------------------------------
(est1 stored)

. eststo: regress support01 i.treat_3##i.stereo i.educ if experiment == "Experiment 2"

      Source |       SS           df       MS      Number of obs   =     1,581
-------------+----------------------------------   F(8, 1572)      =     10.67
       Model |   6.3055716         8   .78819645   Prob > F        =    0.0000
    Residual |  116.131367     1,572  .073874915   R-squared       =    0.0515
-------------+----------------------------------   Adj R-squared   =    0.0467
       Total |  122.436939     1,580  .077491733   Root MSE        =     .2718

--------------------------------------------------------------------------------------------------
                       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         treat_3 |
                      Party Cue  |   .0924161     .02367     3.90   0.000      .045988    .1388441
              Cue w/Insinuation  |   .0603543   .0236811     2.55   0.011     .0139044    .1068043
                                 |
                          stereo |
                  Stereotypical  |   .1311835   .0237176     5.53   0.000      .084662    .1777049
                                 |
                  treat_3#stereo |
        Party Cue#Stereotypical  |  -.0267287   .0335243    -0.80   0.425    -.0924857    .0390284
Cue w/Insinuation#Stereotypical  |  -.0481717   .0334923    -1.44   0.151     -.113866    .0175227
                                 |
                            educ |
                   Some College  |  -.0156663    .022927    -0.68   0.495    -.0606371    .0293045
                             BA  |   -.004952   .0227341    -0.22   0.828    -.0495444    .0396404
                        Post-BA  |  -.0229861     .02722    -0.84   0.399    -.0763774    .0304052
                                 |
                           _cons |   .4081212   .0248428    16.43   0.000     .3593927    .4568497
--------------------------------------------------------------------------------------------------
(est2 stored)

. eststo: regress support01 i.treat_3##i.stereo i.educ if experiment == "Experiment 3"

      Source |       SS           df       MS      Number of obs   =     2,453
-------------+----------------------------------   F(8, 2444)      =     16.56
       Model |  11.1120331         8  1.38900413   Prob > F        =    0.0000
    Residual |  204.978403     2,444   .08387005   R-squared       =    0.0514
-------------+----------------------------------   Adj R-squared   =    0.0483
       Total |  216.090436     2,452  .088128237   Root MSE        =     .2896

--------------------------------------------------------------------------------------------------
                       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         treat_3 |
                      Party Cue  |    .102982   .0257725     4.00   0.000     .0524437    .1535203
              Cue w/Insinuation  |   .0155476   .0212717     0.73   0.465    -.0261649      .05726
                                 |
                          stereo |
                  Stereotypical  |   .0741511   .0261213     2.84   0.005      .022929    .1253732
                                 |
                  treat_3#stereo |
        Party Cue#Stereotypical  |  -.0086989   .0369203    -0.24   0.814    -.0810971    .0636994
Cue w/Insinuation#Stereotypical  |   .0341558   .0301984     1.13   0.258    -.0250613    .0933729
                                 |
                            educ |
                   Some College  |   .0000506   .0144979     0.00   0.997    -.0283788    .0284801
                             BA  |   .0186138   .0166415     1.12   0.263     -.014019    .0512466
                        Post-BA  |   .1187443   .0203036     5.85   0.000     .0789302    .1585584
                                 |
                           _cons |   .4209413   .0207087    20.33   0.000     .3803329    .4615497
--------------------------------------------------------------------------------------------------
(est3 stored)

. 
. /*****Table******/
. esttab using "table_oe2.rtf", replace ///
>         onecell label nobaselevels b(2) se star(* 0.05 ** 0.01 *** 0.001) ///
>         mtitles("Pooled" "Exp 2." "Exp. 3") ///
>         title("{\b Table OE2:} Figure 2 Analyses, Controlling for Education") ///
>         addnotes("The baseline category for the cue treatment is the No Cue condition. The base category for policy stereotypicality is counter-stereotypical. The bas
> e for experiment is Experiment 2.")
(note: file table_oe2.rtf not found)
(output written to table_oe2.rtf)

. 
. /******Calculate marginal effect******/
. *Both
. drop _est_*

. 
. eststo clear

. regress support01 i.treat_3##i.stereo i.exp i.educ

      Source |       SS           df       MS      Number of obs   =     4,034
-------------+----------------------------------   F(9, 4024)      =     21.30
       Model |  15.4048261         9  1.71164735   Prob > F        =    0.0000
    Residual |  323.289098     4,024  .080340233   R-squared       =    0.0455
-------------+----------------------------------   Adj R-squared   =    0.0433
       Total |  338.693924     4,033  .083980641   Root MSE        =    .28344

--------------------------------------------------------------------------------------------------
                       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         treat_3 |
                      Party Cue  |   .0968062   .0176366     5.49   0.000     .0622287    .1313836
              Cue w/Insinuation  |   .0344903   .0155787     2.21   0.027     .0039475    .0650331
                                 |
                          stereo |
                  Stereotypical  |   .1045753   .0177708     5.88   0.000     .0697347    .1394159
                                 |
                  treat_3#stereo |
        Party Cue#Stereotypical  |  -.0187358    .025122    -0.75   0.456    -.0679888    .0305173
Cue w/Insinuation#Stereotypical  |  -.0043594   .0218365    -0.20   0.842     -.047171    .0384521
                                 |
                             exp |
                   Experiment 3  |   .0208987   .0097207     2.15   0.032     .0018408    .0399566
                                 |
                            educ |
                   Some College  |  -.0002622   .0121138    -0.02   0.983     -.024012    .0234876
                             BA  |   .0161832   .0130402     1.24   0.215    -.0093828    .0417491
                        Post-BA  |   .0641977   .0159657     4.02   0.000     .0328962    .0954992
                                 |
                           _cons |   .3944001   .0165491    23.83   0.000     .3619547    .4268456
--------------------------------------------------------------------------------------------------

.         margins, dydx(treat_3) by(stereo) post

Average marginal effects                        Number of obs     =      4,034
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 2.treat_3 3.treat_3
over         : stereo

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
1.treat_3              |  (base outcome)
-----------------------+----------------------------------------------------------------
2.treat_3              |
                stereo |
Counter-Stereotypical  |   .0968062   .0176366     5.49   0.000     .0622287    .1313836
        Stereotypical  |   .0780704   .0178922     4.36   0.000     .0429918     .113149
-----------------------+----------------------------------------------------------------
3.treat_3              |
                stereo |
Counter-Stereotypical  |   .0344903   .0155787     2.21   0.027     .0039475    .0650331
        Stereotypical  |   .0301309   .0156728     1.92   0.055    -.0005964    .0608581
----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.         estimates store m1

.         
. regress support01 i.treat_3##i.stereo i.educ if experiment == "Experiment 2"

      Source |       SS           df       MS      Number of obs   =     1,581
-------------+----------------------------------   F(8, 1572)      =     10.67
       Model |   6.3055716         8   .78819645   Prob > F        =    0.0000
    Residual |  116.131367     1,572  .073874915   R-squared       =    0.0515
-------------+----------------------------------   Adj R-squared   =    0.0467
       Total |  122.436939     1,580  .077491733   Root MSE        =     .2718

--------------------------------------------------------------------------------------------------
                       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         treat_3 |
                      Party Cue  |   .0924161     .02367     3.90   0.000      .045988    .1388441
              Cue w/Insinuation  |   .0603543   .0236811     2.55   0.011     .0139044    .1068043
                                 |
                          stereo |
                  Stereotypical  |   .1311835   .0237176     5.53   0.000      .084662    .1777049
                                 |
                  treat_3#stereo |
        Party Cue#Stereotypical  |  -.0267287   .0335243    -0.80   0.425    -.0924857    .0390284
Cue w/Insinuation#Stereotypical  |  -.0481717   .0334923    -1.44   0.151     -.113866    .0175227
                                 |
                            educ |
                   Some College  |  -.0156663    .022927    -0.68   0.495    -.0606371    .0293045
                             BA  |   -.004952   .0227341    -0.22   0.828    -.0495444    .0396404
                        Post-BA  |  -.0229861     .02722    -0.84   0.399    -.0763774    .0304052
                                 |
                           _cons |   .4081212   .0248428    16.43   0.000     .3593927    .4568497
--------------------------------------------------------------------------------------------------

.         margins, dydx(treat_3) by(stereo) post

Average marginal effects                        Number of obs     =      1,581
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 2.treat_3 3.treat_3
over         : stereo

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
1.treat_3              |  (base outcome)
-----------------------+----------------------------------------------------------------
2.treat_3              |
                stereo |
Counter-Stereotypical  |   .0924161     .02367     3.90   0.000      .045988    .1388441
        Stereotypical  |   .0656874   .0237527     2.77   0.006      .019097    .1122778
-----------------------+----------------------------------------------------------------
3.treat_3              |
                stereo |
Counter-Stereotypical  |   .0603543   .0236811     2.55   0.011     .0139044    .1068043
        Stereotypical  |   .0121826   .0236855     0.51   0.607    -.0342759    .0586412
----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.         estimates store m2

.         
. regress support01 i.treat_3##i.stereo i.educ if experiment == "Experiment 3"

      Source |       SS           df       MS      Number of obs   =     2,453
-------------+----------------------------------   F(8, 2444)      =     16.56
       Model |  11.1120331         8  1.38900413   Prob > F        =    0.0000
    Residual |  204.978403     2,444   .08387005   R-squared       =    0.0514
-------------+----------------------------------   Adj R-squared   =    0.0483
       Total |  216.090436     2,452  .088128237   Root MSE        =     .2896

--------------------------------------------------------------------------------------------------
                       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         treat_3 |
                      Party Cue  |    .102982   .0257725     4.00   0.000     .0524437    .1535203
              Cue w/Insinuation  |   .0155476   .0212717     0.73   0.465    -.0261649      .05726
                                 |
                          stereo |
                  Stereotypical  |   .0741511   .0261213     2.84   0.005      .022929    .1253732
                                 |
                  treat_3#stereo |
        Party Cue#Stereotypical  |  -.0086989   .0369203    -0.24   0.814    -.0810971    .0636994
Cue w/Insinuation#Stereotypical  |   .0341558   .0301984     1.13   0.258    -.0250613    .0933729
                                 |
                            educ |
                   Some College  |   .0000506   .0144979     0.00   0.997    -.0283788    .0284801
                             BA  |   .0186138   .0166415     1.12   0.263     -.014019    .0512466
                        Post-BA  |   .1187443   .0203036     5.85   0.000     .0789302    .1585584
                                 |
                           _cons |   .4209413   .0207087    20.33   0.000     .3803329    .4615497
--------------------------------------------------------------------------------------------------

.         margins, dydx(treat_3) by(stereo) post

Average marginal effects                        Number of obs     =      2,453
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 2.treat_3 3.treat_3
over         : stereo

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |      dy/dx   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
1.treat_3              |  (base outcome)
-----------------------+----------------------------------------------------------------
2.treat_3              |
                stereo |
Counter-Stereotypical  |    .102982   .0257725     4.00   0.000     .0524437    .1535203
        Stereotypical  |   .0942831   .0264471     3.56   0.000     .0424222    .1461441
-----------------------+----------------------------------------------------------------
3.treat_3              |
                stereo |
Counter-Stereotypical  |   .0155476   .0212717     0.73   0.465    -.0261649      .05726
        Stereotypical  |   .0497033   .0214313     2.32   0.020      .007678    .0917287
----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.         estimates store m3

.         
. esttab m1 m2 m3  using "table_oe2_margins.rtf", replace ///
>         onecell label nobaselevels b(2) ci ///
>         mtitles("Both Experiments" "Experiment 2" "Experiment 3") ///
>         title("{\b Table OE3:} Marginal Effects From Table OE2")
(note: file table_oe2_margins.rtf not found)
(output written to table_oe2_margins.rtf)

.         
. /******differences******/       
.         
. regress support01 i.treat_3##i.stereo i.exp i.educ 

      Source |       SS           df       MS      Number of obs   =     4,034
-------------+----------------------------------   F(9, 4024)      =     21.30
       Model |  15.4048261         9  1.71164735   Prob > F        =    0.0000
    Residual |  323.289098     4,024  .080340233   R-squared       =    0.0455
-------------+----------------------------------   Adj R-squared   =    0.0433
       Total |  338.693924     4,033  .083980641   Root MSE        =    .28344

--------------------------------------------------------------------------------------------------
                       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         treat_3 |
                      Party Cue  |   .0968062   .0176366     5.49   0.000     .0622287    .1313836
              Cue w/Insinuation  |   .0344903   .0155787     2.21   0.027     .0039475    .0650331
                                 |
                          stereo |
                  Stereotypical  |   .1045753   .0177708     5.88   0.000     .0697347    .1394159
                                 |
                  treat_3#stereo |
        Party Cue#Stereotypical  |  -.0187358    .025122    -0.75   0.456    -.0679888    .0305173
Cue w/Insinuation#Stereotypical  |  -.0043594   .0218365    -0.20   0.842     -.047171    .0384521
                                 |
                             exp |
                   Experiment 3  |   .0208987   .0097207     2.15   0.032     .0018408    .0399566
                                 |
                            educ |
                   Some College  |  -.0002622   .0121138    -0.02   0.983     -.024012    .0234876
                             BA  |   .0161832   .0130402     1.24   0.215    -.0093828    .0417491
                        Post-BA  |   .0641977   .0159657     4.02   0.000     .0328962    .0954992
                                 |
                           _cons |   .3944001   .0165491    23.83   0.000     .3619547    .4268456
--------------------------------------------------------------------------------------------------

.         margins, dydx(treat_3) by(stereo) post coeflegend

Average marginal effects                        Number of obs     =      4,034
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 2.treat_3 3.treat_3
over         : stereo

----------------------------------------------------------------------------------------
                       |      dy/dx  Legend
-----------------------+----------------------------------------------------------------
1.treat_3              |  (base outcome)
-----------------------+----------------------------------------------------------------
2.treat_3              |
                stereo |
Counter-Stereotypical  |   .0968062  _b[2.treat_3:1bn.stereo]
        Stereotypical  |   .0780704  _b[2.treat_3:2.stereo]
-----------------------+----------------------------------------------------------------
3.treat_3              |
                stereo |
Counter-Stereotypical  |   .0344903  _b[3.treat_3:1bn.stereo]
        Stereotypical  |   .0301309  _b[3.treat_3:2.stereo]
----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.         *stereotypical
.         lincom  _b[2.treat_3:2.stereo] -  _b[3.treat_3:2.stereo]        

 ( 1)  [2.treat_3]2.stereo - [3.treat_3]2.stereo = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0479395   .0157697     3.04   0.002     .0170223    .0788568
------------------------------------------------------------------------------

.         *counter-stereotypical
.         lincom _b[2.treat_3:1bn.stereo] - _b[3.treat_3:1bn.stereo]

 ( 1)  [2.treat_3]1bn.stereo - [3.treat_3]1bn.stereo = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0623159   .0154656     4.03   0.000     .0319948    .0926369
------------------------------------------------------------------------------

. 
. regress support01 i.treat_3##i.stereo  i.educ if experiment == "Experiment 2"

      Source |       SS           df       MS      Number of obs   =     1,581
-------------+----------------------------------   F(8, 1572)      =     10.67
       Model |   6.3055716         8   .78819645   Prob > F        =    0.0000
    Residual |  116.131367     1,572  .073874915   R-squared       =    0.0515
-------------+----------------------------------   Adj R-squared   =    0.0467
       Total |  122.436939     1,580  .077491733   Root MSE        =     .2718

--------------------------------------------------------------------------------------------------
                       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         treat_3 |
                      Party Cue  |   .0924161     .02367     3.90   0.000      .045988    .1388441
              Cue w/Insinuation  |   .0603543   .0236811     2.55   0.011     .0139044    .1068043
                                 |
                          stereo |
                  Stereotypical  |   .1311835   .0237176     5.53   0.000      .084662    .1777049
                                 |
                  treat_3#stereo |
        Party Cue#Stereotypical  |  -.0267287   .0335243    -0.80   0.425    -.0924857    .0390284
Cue w/Insinuation#Stereotypical  |  -.0481717   .0334923    -1.44   0.151     -.113866    .0175227
                                 |
                            educ |
                   Some College  |  -.0156663    .022927    -0.68   0.495    -.0606371    .0293045
                             BA  |   -.004952   .0227341    -0.22   0.828    -.0495444    .0396404
                        Post-BA  |  -.0229861     .02722    -0.84   0.399    -.0763774    .0304052
                                 |
                           _cons |   .4081212   .0248428    16.43   0.000     .3593927    .4568497
--------------------------------------------------------------------------------------------------

.         margins, dydx(treat_3) by(stereo) post coeflegend

Average marginal effects                        Number of obs     =      1,581
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 2.treat_3 3.treat_3
over         : stereo

----------------------------------------------------------------------------------------
                       |      dy/dx  Legend
-----------------------+----------------------------------------------------------------
1.treat_3              |  (base outcome)
-----------------------+----------------------------------------------------------------
2.treat_3              |
                stereo |
Counter-Stereotypical  |   .0924161  _b[2.treat_3:1bn.stereo]
        Stereotypical  |   .0656874  _b[2.treat_3:2.stereo]
-----------------------+----------------------------------------------------------------
3.treat_3              |
                stereo |
Counter-Stereotypical  |   .0603543  _b[3.treat_3:1bn.stereo]
        Stereotypical  |   .0121826  _b[3.treat_3:2.stereo]
----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.         *stereotypical
.         lincom  _b[2.treat_3:2.stereo] -  _b[3.treat_3:2.stereo]        

 ( 1)  [2.treat_3]2.stereo - [3.treat_3]2.stereo = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0535047   .0236367     2.26   0.024      .007142    .0998675
------------------------------------------------------------------------------

.         *counter-stereotypical
.         lincom _b[2.treat_3:1bn.stereo] - _b[3.treat_3:1bn.stereo]

 ( 1)  [2.treat_3]1bn.stereo - [3.treat_3]1bn.stereo = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0320618   .0237148     1.35   0.177    -.0144542    .0785777
------------------------------------------------------------------------------

. 
. 
. regress support01 i.treat_3##i.stereo  i.educ if experiment == "Experiment 3"

      Source |       SS           df       MS      Number of obs   =     2,453
-------------+----------------------------------   F(8, 2444)      =     16.56
       Model |  11.1120331         8  1.38900413   Prob > F        =    0.0000
    Residual |  204.978403     2,444   .08387005   R-squared       =    0.0514
-------------+----------------------------------   Adj R-squared   =    0.0483
       Total |  216.090436     2,452  .088128237   Root MSE        =     .2896

--------------------------------------------------------------------------------------------------
                       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         treat_3 |
                      Party Cue  |    .102982   .0257725     4.00   0.000     .0524437    .1535203
              Cue w/Insinuation  |   .0155476   .0212717     0.73   0.465    -.0261649      .05726
                                 |
                          stereo |
                  Stereotypical  |   .0741511   .0261213     2.84   0.005      .022929    .1253732
                                 |
                  treat_3#stereo |
        Party Cue#Stereotypical  |  -.0086989   .0369203    -0.24   0.814    -.0810971    .0636994
Cue w/Insinuation#Stereotypical  |   .0341558   .0301984     1.13   0.258    -.0250613    .0933729
                                 |
                            educ |
                   Some College  |   .0000506   .0144979     0.00   0.997    -.0283788    .0284801
                             BA  |   .0186138   .0166415     1.12   0.263     -.014019    .0512466
                        Post-BA  |   .1187443   .0203036     5.85   0.000     .0789302    .1585584
                                 |
                           _cons |   .4209413   .0207087    20.33   0.000     .3803329    .4615497
--------------------------------------------------------------------------------------------------

.         margins, dydx(treat_3) by(stereo) post coeflegend

Average marginal effects                        Number of obs     =      2,453
Model VCE    : OLS

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 2.treat_3 3.treat_3
over         : stereo

----------------------------------------------------------------------------------------
                       |      dy/dx  Legend
-----------------------+----------------------------------------------------------------
1.treat_3              |  (base outcome)
-----------------------+----------------------------------------------------------------
2.treat_3              |
                stereo |
Counter-Stereotypical  |    .102982  _b[2.treat_3:1bn.stereo]
        Stereotypical  |   .0942831  _b[2.treat_3:2.stereo]
-----------------------+----------------------------------------------------------------
3.treat_3              |
                stereo |
Counter-Stereotypical  |   .0155476  _b[3.treat_3:1bn.stereo]
        Stereotypical  |   .0497033  _b[3.treat_3:2.stereo]
----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.         *stereotypical
.         lincom  _b[2.treat_3:2.stereo] -  _b[3.treat_3:2.stereo]        

 ( 1)  [2.treat_3]2.stereo - [3.treat_3]2.stereo = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0445798    .021759     2.05   0.041     .0019119    .0872477
------------------------------------------------------------------------------

.         *counter-stereotypical
.         lincom _b[2.treat_3:1bn.stereo] - _b[3.treat_3:1bn.stereo]

 ( 1)  [2.treat_3]1bn.stereo - [3.treat_3]1bn.stereo = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0874344   .0209222     4.18   0.000     .0464074    .1284615
------------------------------------------------------------------------------

. 
.         
.         
.         
. /**********************************************
> Figure 3
> **********************************************/
. 
. drop _est_*

. 
. /****Models*****/
. eststo clear

. regress support01 i.treat_5 i.stereo i.educ

      Source |       SS           df       MS      Number of obs   =     2,453
-------------+----------------------------------   F(8, 2444)      =     16.71
       Model |   11.205669         8  1.40070862   Prob > F        =    0.0000
    Residual |  204.884767     2,444  .083831738   R-squared       =    0.0519
-------------+----------------------------------   Adj R-squared   =    0.0488
       Total |  216.090436     2,452  .088128237   Root MSE        =    .28954

----------------------------------------------------------------------------------
       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         treat_5 |
      Party Cue  |   .0992185   .0184551     5.38   0.000     .0630292    .1354078
Interest Groups  |   .0503157   .0185966     2.71   0.007     .0138489    .0867825
      Companies  |   .0142411   .0184593     0.77   0.440    -.0219565    .0504386
   Labor Unions  |   .0332734   .0184509     1.80   0.071    -.0029075    .0694544
                 |
          stereo |
  Stereotypical  |   .0928677    .011698     7.94   0.000     .0699287    .1158068
                 |
            educ |
   Some College  |   .0007004      .0145     0.05   0.961    -.0277331    .0291339
             BA  |   .0172798   .0166388     1.04   0.299    -.0153477    .0499073
        Post-BA  |   .1186862   .0202937     5.85   0.000     .0788915    .1584809
                 |
           _cons |   .4117182   .0171081    24.07   0.000     .3781704     .445266
----------------------------------------------------------------------------------

.         estimates store m1

.         lincom _b[2.treat_5] - _b[3.treat_5]

 ( 1)  2.treat_5 - 3.treat_5 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0489028   .0185821     2.63   0.009     .0124645    .0853411
------------------------------------------------------------------------------

.         estadd scalar diff1 = r(estimate)

added scalar:
              e(diff1) =  .04890279

.         estadd scalar sig1 = r(t)

added scalar:
               e(sig1) =  2.6317119

.         lincom _b[2.treat_5] - _b[4.treat_5]

 ( 1)  2.treat_5 - 4.treat_5 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0849775   .0184481     4.61   0.000      .048802    .1211529
------------------------------------------------------------------------------

.         estadd scalar diff2 = r(estimate)

added scalar:
              e(diff2) =  .08497746

.         estadd scalar sig2 = r(t)

added scalar:
               e(sig2) =  4.6063109

.         lincom _b[2.treat_5] - _b[5.treat_5]

 ( 1)  2.treat_5 - 5.treat_5 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0659451   .0184396     3.58   0.000     .0297863    .1021039
------------------------------------------------------------------------------

.         estadd scalar diff3 = r(estimate)

added scalar:
              e(diff3) =  .06594508

.         estadd scalar sig3 = r(t)

added scalar:
               e(sig3) =  3.576281

.         
. regress support01 i.treat_5 i.stereo i.educ if pid == 1

      Source |       SS           df       MS      Number of obs   =     1,080
-------------+----------------------------------   F(8, 1071)      =     11.40
       Model |  8.27462765         8  1.03432846   Prob > F        =    0.0000
    Residual |  97.1709187     1,071  .090729149   R-squared       =    0.0785
-------------+----------------------------------   Adj R-squared   =    0.0716
       Total |  105.445546     1,079  .097725251   Root MSE        =    .30121

----------------------------------------------------------------------------------
       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         treat_5 |
      Party Cue  |   .0663924   .0290879     2.28   0.023     .0093166    .1234682
Interest Groups  |   .0350499   .0292577     1.20   0.231     -.022359    .0924587
      Companies  |  -.0563829   .0289201    -1.95   0.051    -.1131294    .0003636
   Labor Unions  |  -.0084812   .0288998    -0.29   0.769    -.0651879    .0482256
                 |
          stereo |
  Stereotypical  |    .135333   .0183615     7.37   0.000     .0993044    .1713615
                 |
            educ |
   Some College  |  -.0037378   .0227866    -0.16   0.870    -.0484492    .0409736
             BA  |   .0009776   .0265717     0.04   0.971    -.0511609    .0531161
        Post-BA  |   .1148911   .0307457     3.74   0.000     .0545625    .1752198
                 |
           _cons |   .3988002   .0273259    14.59   0.000     .3451818    .4524185
----------------------------------------------------------------------------------

.         estimates store m2

.         lincom _b[2.treat_5] - _b[3.treat_5]

 ( 1)  2.treat_5 - 3.treat_5 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0313426   .0293284     1.07   0.285    -.0262051    .0888902
------------------------------------------------------------------------------

.         estadd scalar diff1 = r(estimate)

added scalar:
              e(diff1) =  .03134256

.         estadd scalar sig1 = r(t)

added scalar:
               e(sig1) =  1.0686762

.         lincom _b[2.treat_5] - _b[4.treat_5]

 ( 1)  2.treat_5 - 4.treat_5 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .1227754   .0288653     4.25   0.000     .0661363    .1794144
------------------------------------------------------------------------------

.         estadd scalar diff2 = r(estimate)

added scalar:
              e(diff2) =  .12277535

.         estadd scalar sig2 = r(t)

added scalar:
               e(sig2) =  4.2533846

.         lincom _b[2.treat_5] - _b[5.treat_5]

 ( 1)  2.treat_5 - 5.treat_5 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0748736   .0289403     2.59   0.010     .0180874    .1316598
------------------------------------------------------------------------------

.         estadd scalar diff3 = r(estimate)

added scalar:
              e(diff3) =  .07487358

.         estadd scalar sig3 = r(t)

added scalar:
               e(sig3) =  2.5871707

.         
. regress support01 i.treat_5 i.stereo i.educ if pid == 2

      Source |       SS           df       MS      Number of obs   =     1,373
-------------+----------------------------------   F(8, 1364)      =      8.33
       Model |  5.07342095         8  .634177618   Prob > F        =    0.0000
    Residual |  103.811459     1,364  .076108108   R-squared       =    0.0466
-------------+----------------------------------   Adj R-squared   =    0.0410
       Total |   108.88488     1,372  .079362157   Root MSE        =    .27588

----------------------------------------------------------------------------------
       support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
         treat_5 |
      Party Cue  |   .1251083   .0234865     5.33   0.000     .0790347    .1711819
Interest Groups  |    .063542   .0236432     2.69   0.007      .017161    .1099229
      Companies  |   .0714807   .0235776     3.03   0.002     .0252284     .117733
   Labor Unions  |   .0675815   .0235466     2.87   0.004     .0213901     .113773
                 |
          stereo |
  Stereotypical  |   .0569725   .0148954     3.82   0.000     .0277521    .0861928
                 |
            educ |
   Some College  |   .0024956   .0184607     0.14   0.892    -.0337188      .03871
             BA  |   .0240172   .0209437     1.15   0.252    -.0170682    .0651025
        Post-BA  |    .126469   .0266838     4.74   0.000     .0741233    .1788147
                 |
           _cons |   .4242914   .0215117    19.72   0.000     .3820919     .466491
----------------------------------------------------------------------------------

.         estimates store m3

.         lincom _b[2.treat_5] - _b[3.treat_5]

 ( 1)  2.treat_5 - 3.treat_5 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0615664   .0235776     2.61   0.009      .015314    .1078187
------------------------------------------------------------------------------

.         estadd scalar diff1 = r(estimate)

added scalar:
              e(diff1) =  .06156636

.         estadd scalar sig1 = r(t)

added scalar:
               e(sig1) =  2.6112173

.         lincom _b[2.treat_5] - _b[4.treat_5]

 ( 1)  2.treat_5 - 4.treat_5 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0536276    .023577     2.27   0.023     .0073764    .0998788
------------------------------------------------------------------------------

.         estadd scalar diff2 = r(estimate)

added scalar:
              e(diff2) =  .0536276

.         estadd scalar sig2 = r(t)

added scalar:
               e(sig2) =  2.2745694

.         lincom _b[2.treat_5] - _b[5.treat_5]

 ( 1)  2.treat_5 - 5.treat_5 = 0

------------------------------------------------------------------------------
   support01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0575268   .0234974     2.45   0.014     .0114319    .1036217
------------------------------------------------------------------------------

.         estadd scalar diff3 = r(estimate)

added scalar:
              e(diff3) =  .05752677

.         estadd scalar sig3 = r(t)

added scalar:
               e(sig3) =  2.4482216

. 
. /*****Table*****/
. 
. esttab m1 m2 m3 using "table_oe4.rtf",  replace ///
>         onecell label nobaselevels b(2) se star(* 0.05 ** 0.01 *** 0.001) ///
>         mtitles("All Partisans" "Republicans" "Democrats") ///
>         title("{\b Table OB4:} Figure 3 Analyses Controlling for Education")  ///
>         stats(N diff1 sig1 diff2 sig2 diff3 sig3)
(note: file table_oe4.rtf not found)
(output written to table_oe4.rtf)

.         
.         
.                 
. /**********************************************
> Figure 4 (Argument Rating Differences)
> **********************************************/
. drop _est_*

. 
. eststo clear

. regress argdiff i.treat_3 i.polissue i.educ

      Source |       SS           df       MS      Number of obs   =     5,035
-------------+----------------------------------   F(9, 5025)      =     14.27
       Model |  4.47598298         9  .497331442   Prob > F        =    0.0000
    Residual |  175.073978     5,025  .034840593   R-squared       =    0.0249
-------------+----------------------------------   Adj R-squared   =    0.0232
       Total |  179.549961     5,034  .035667454   Root MSE        =    .18666

---------------------------------------------------------------------------------------------
                    argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                    treat_3 |
                 Party Cue  |   .0598224   .0071941     8.32   0.000     .0457188    .0739261
         Cue w/Insinuation  |   .0323121   .0065312     4.95   0.000     .0195082    .0451161
                            |
                   polissue |
Exp 2: Conservative Change  |  -.0486166   .0088693    -5.48   0.000    -.0660042    -.031229
     Exp 2: Liberal Change  |  -.0330049   .0088547    -3.73   0.000    -.0503641   -.0156458
Exp 3: Conservative Change  |  -.0405234   .0081709    -4.96   0.000     -.056542   -.0245049
     Exp 3: Liberal Change  |   -.058381   .0081407    -7.17   0.000    -.0743403   -.0424217
                            |
                       educ |
              Some College  |   .0059223   .0073859     0.80   0.423    -.0085573    .0204018
                        BA  |   -.000105   .0078095    -0.01   0.989     -.015415     .015205
                   Post-BA  |   .0023368   .0094782     0.25   0.805    -.0162445    .0209181
                            |
                      _cons |   .5145869   .0092257    55.78   0.000     .4965006    .5326732
---------------------------------------------------------------------------------------------

.         estimates store m1

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
     argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0275103   .0065514     4.20   0.000     .0146668    .0403539
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .02751033

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  4.1991771

.         
. regress argdiff i.treat_3 i.educ if experiment == "Experiment 1"

      Source |       SS           df       MS      Number of obs   =     1,013
-------------+----------------------------------   F(5, 1007)      =      6.08
       Model |   1.1319661         5   .22639322   Prob > F        =    0.0000
    Residual |  37.4843688     1,007  .037223802   R-squared       =    0.0293
-------------+----------------------------------   Adj R-squared   =    0.0245
       Total |  38.6163349     1,012  .038158434   Root MSE        =    .19293

------------------------------------------------------------------------------------
           argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           treat_3 |
        Party Cue  |    .079679   .0150633     5.29   0.000     .0501199     .109238
Cue w/Insinuation  |   .0529358   .0146981     3.60   0.000     .0240933    .0817782
                   |
              educ |
     Some College  |   .0050235   .0202048     0.25   0.804    -.0346247    .0446718
               BA  |  -.0044264   .0200455    -0.22   0.825    -.0437622    .0349095
          Post-BA  |   .0011694   .0233437     0.05   0.960    -.0446385    .0469772
                   |
             _cons |     .50323   .0190884    26.36   0.000     .4657724    .5406877
------------------------------------------------------------------------------------

.         estimates store m2

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
     argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0267432   .0149096     1.79   0.073    -.0025143    .0560007
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .0267432

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  1.7936842

.         
. regress argdiff i.treat_3 i.educ i.treat_policy if experiment == "Experiment 2"

      Source |       SS           df       MS      Number of obs   =     1,581
-------------+----------------------------------   F(6, 1574)      =      4.93
       Model |  1.11270305         6  .185450508   Prob > F        =    0.0001
    Residual |  59.2656176     1,574   .03765287   R-squared       =    0.0184
-------------+----------------------------------   Adj R-squared   =    0.0147
       Total |  60.3783207     1,580  .038214127   Root MSE        =    .19404

------------------------------------------------------------------------------------
           argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           treat_3 |
        Party Cue  |   .0552161   .0119737     4.61   0.000     .0317301    .0787022
Cue w/Insinuation  |   .0405015   .0119505     3.39   0.001      .017061     .063942
                   |
              educ |
     Some College  |   -.008864   .0164109    -0.54   0.589    -.0410536    .0233256
               BA  |  -.0017585   .0162575    -0.11   0.914    -.0336472    .0301302
          Post-BA  |  -.0335719   .0195019    -1.72   0.085    -.0718243    .0046805
                   |
      treat_policy |
   Liberal Change  |   .0172639   .0097885     1.76   0.078     -.001936    .0364638
             _cons |   .4747625   .0162443    29.23   0.000     .4428998    .5066253
------------------------------------------------------------------------------------

.         estimates store m3

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
     argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0147146   .0119579     1.23   0.219    -.0087405    .0381698
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .01471465

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  1.2305358

.         
. regress argdiff i.treat_3 i.educ i.treat_policy if experiment == "Experiment 3"

      Source |       SS           df       MS      Number of obs   =     2,441
-------------+----------------------------------   F(6, 2434)      =      5.77
       Model |  1.10484394         6  .184140657   Prob > F        =    0.0000
    Residual |  77.7358079     2,434  .031937472   R-squared       =    0.0140
-------------+----------------------------------   Adj R-squared   =    0.0116
       Total |  78.8406519     2,440  .032311743   Root MSE        =    .17871

------------------------------------------------------------------------------------
           argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           treat_3 |
        Party Cue  |   .0525254   .0114138     4.60   0.000     .0301436    .0749072
Cue w/Insinuation  |   .0175142   .0093313     1.88   0.061    -.0007839    .0358122
                   |
              educ |
     Some College  |   .0107968   .0089653     1.20   0.229    -.0067836    .0283773
               BA  |  -.0060328   .0102862    -0.59   0.558    -.0262034    .0141377
          Post-BA  |   .0238166   .0125407     1.90   0.058    -.0007749    .0484081
                   |
      treat_policy |
   Liberal Change  |  -.0176681   .0072366    -2.44   0.015    -.0318586   -.0034777
             _cons |   .4812036   .0105002    45.83   0.000     .4606134    .5017938
------------------------------------------------------------------------------------

.         estimates store m4

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
     argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0350112   .0093339     3.75   0.000     .0167081    .0533144
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .03501124

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  3.7509843

. 
. /*******Table******/
. esttab m1 m2 m3 m4 using "table_oe5_table.rtf", replace ///
>         onecell label nobaselevels b(2) se star(* 0.05 ** 0.01 *** 0.001) ///
>         mtitles("Pooled" "Exp. 1" "Exp. 2" "Exp. 3") ///
>         title("{\b Table OE5:} Figure 4 Analyses (Argument Ratings) with Education Controls") ///
>         stats(N diff sig) ///
>         addnotes("The baseline category for the cue treatment is the No Cue condition. The base category for experiment/policy in the Pooled model is Experiment 1. Th
> e base category for the policy treatment in Exp. 2 and 3 models is the Conservative Change condition")
(note: file table_oe5_table.rtf not found)
(output written to table_oe5_table.rtf)

.         
. /**********************************************
> Figure 4 (Inferences and Proximity)
> **********************************************/
. 
. summ inferences

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  inferences |      2,460    4.054806    1.217055          1          7

. gen inf01 = (inferences - r(min))/(r(max)-r(min))
(3,691 missing values generated)

. 
. 
. drop _est_*

. eststo clear

. 
. regress inf01 i.treat_3 i.stereo i.educ 

      Source |       SS           df       MS      Number of obs   =     2,455
-------------+----------------------------------   F(6, 2448)      =     12.37
       Model |  2.96659262         6  .494432104   Prob > F        =    0.0000
    Residual |  97.8304274     2,448   .03996341   R-squared       =    0.0294
-------------+----------------------------------   Adj R-squared   =    0.0271
       Total |   100.79702     2,454   .04107458   Root MSE        =    .19991

------------------------------------------------------------------------------------
             inf01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           treat_3 |
        Party Cue  |   .0434749   .0127422     3.41   0.001     .0184884    .0684615
Cue w/Insinuation  |   .0273514   .0104188     2.63   0.009     .0069208     .047782
                   |
            stereo |
    Stereotypical  |   .0408346   .0080734     5.06   0.000     .0250031     .056666
                   |
              educ |
     Some College  |   .0169353   .0100024     1.69   0.091    -.0026787    .0365494
               BA  |    .030031   .0114791     2.62   0.009     .0075213    .0525407
          Post-BA  |   .0853853   .0139849     6.11   0.000     .0579619    .1128088
                   |
             _cons |   .4406559   .0118107    37.31   0.000     .4174959    .4638158
------------------------------------------------------------------------------------

.         estimates store m1

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
       inf01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0161235   .0104084     1.55   0.121    -.0042867    .0365338
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .01612354

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  1.5490863

.         
. regress prox01 i.treat_3 i.stereo i.educ 

      Source |       SS           df       MS      Number of obs   =     2,450
-------------+----------------------------------   F(6, 2443)      =      8.07
       Model |  3.39068505         6  .565114176   Prob > F        =    0.0000
    Residual |  171.100426     2,443  .070037014   R-squared       =    0.0194
-------------+----------------------------------   Adj R-squared   =    0.0170
       Total |  174.491111     2,449  .071249943   Root MSE        =    .26465

------------------------------------------------------------------------------------
            prox01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           treat_3 |
        Party Cue  |   .0682276   .0168685     4.04   0.000     .0351495    .1013057
Cue w/Insinuation  |   .0317141   .0137991     2.30   0.022     .0046549    .0587732
                   |
            stereo |
    Stereotypical  |   .0585279   .0106986     5.47   0.000     .0375486    .0795072
                   |
              educ |
     Some College  |  -.0072715   .0132526    -0.55   0.583    -.0332591     .018716
               BA  |  -.0143206   .0152219    -0.94   0.347    -.0441699    .0155286
          Post-BA  |  -.0242311   .0185439    -1.31   0.191    -.0605945    .0121322
                   |
             _cons |   .6597038   .0156427    42.17   0.000     .6290295    .6903781
------------------------------------------------------------------------------------

.         estimates store m2

.         lincom _b[2.treat_3] - _b[3.treat_3]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
      prox01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0365135   .0137852     2.65   0.008     .0094817    .0635454
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .03651354

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  2.648755

.         
. esttab m1 m2 using "table_oe6_table.rtf", replace ///
>         onecell label nobaselevels b(2) se star(* 0.05 ** 0.01 *** 0.001) ///
>         mtitles("Inferences" "Proximity") ///
>         title("{\b Table OE6:} Figure 4 Analyses (Inferences and Proximity) with Education Controls") ///
>         stats(N diff sig) ///
>         addnotes("The baseline category for the cue treatment is the No Cue condition.")
(note: file table_oe6_table.rtf not found)
(output written to table_oe6_table.rtf)

.         
.         
. log close
      name:  <unnamed>
       log:  C:\Users\jar68\OneDrive\Ongoing Work\Party Cues and Suspicion Paper\Final Datavserse\Online Appendix E\appendix_oe_log.log
  log type:  text
 closed on:  23 Jun 2021, 17:07:31
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
